类别特定数据增强：弥合多类乳腺癌分类中的不平衡
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification
AI总结：
本文提出类别特定数据增强结合ViTNet架构，解决多类乳腺癌分类中的类别不平衡问题，提高欠采样类别的检测率，实现良性或四种恶性亚型的精准分类。
摘要：
乳腺癌是女性中最常见的癌症，在男性中也有发生，每年新诊断的癌症病例中，乳腺癌占比超过十分之一。它也是女性因癌症死亡的第二大常见原因。因此，早期检测和针对性治疗显得尤为必要。早期检测能够提供适当的、基于患者的治疗方案。此外，早期检测还能确定囊肿的类型。本文采用类别级别的数据增强，解决欠采样类别的问题并提高其检测率。该方法提出了两个关键组成部分：对苏木精-伊红染色图像进行结构保持的染色归一化技术上的类别级别数据增强，以及通过迁移学习使用基于Transformer的ViTNet架构进行乳腺癌图像的多类分类。这种结合使得通过先进的图像处理和深度学习技术，能够将乳腺癌图像分类为良性或四种不同的恶性亚型之一，其重点在于类别级别的增强，并针对每个类别的独特特征，提高欠采样类别的分类精度，从而降低与乳腺癌相关的死亡率。本文旨在通过执行多类分类并将图像分类为良性或四种不同类型的恶性乳腺癌之一，来减轻医学专家的负担。
英文摘要：
Breast Cancer is the most common cancer among women, which is also visible in men, and accounts for more than 1 in 10 new cancer diagnoses each year. It is also the second most common cause of women who die from cancer. Hence, it necessitates early detection and tailored treatment. Early detection can provide appropriate and patient-based therapeutic schedules. Moreover, early detection can also provide the type of cyst. This paper employs class-level data augmentation, addressing the undersampled classes and raising their detection rate. This approach suggests two key components: class-level data augmentation on structure-preserving stain normalization techniques to hematoxylin and eosin-stained images and transformer-based ViTNet architecture via transfer learning for multiclass classification of breast cancer images. This merger enables categorizing breast cancer images with advanced image processing and deep learning as either benign or as one of four distinct malignant subtypes by focusing on class-level augmentation and catering to unique characteristics of each class with increasing precision of classification on undersampled classes, which leads to lower mortality rates associated with breast cancer. The paper aims to ease the duties of the medical specialist by operating multiclass classification and categorizing the image into benign or one of four different malignant types of breast cancers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
